This study focused on preoperatively predicting failure to achieve the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) on the KOOS JR in patients undergoing a total knee arthroplasty (TKA). Machine-learning models were able to identify patients at risk of failure to achieve the threshold-based metrics (i.e., MCID and SCB) and other relevant preoperative factors. As such, these models may be used to both improve shared decision-making and help create risk-stratification tools to improve quality assessment of surgical outcomes.
External Collaborators: Jaeyoung Park, PhD; Xiang Zhong, PhD; Chancellor Gray, MD
Partner Institutions: University of Central Florida, University of Florida, Florida State University, and Florida Orthopaedic Institute
